Adaptive Hurst-Sensitive Active Queue Management
Abstract
:1. Introduction
2. State of the Art
3. Hurst Estimation Methods
- —the process is negatively correlated, which means that the Long-Range Dependence does not occur;
- —the process is uncorrelated;
- —the process is positively correlated, which means that the LRD occurs.
4. Adaptive AQM
5. Selection of the AQM Parameters with the Use of Neural Networks
Adaptive Neuron AQM
6. Results
6.1. Fluid Flow Analysis
- is the expected TCP congestion window size (in packets) for the i-th flow. It defines a number of packets that may be sent without waiting for the acknowledgements of the reception of previous packets;
- is the round-trip time, , the sum denotes the total input flow to the congestion router;
- q is queue length (in packets);
- C is link capacity (packets/time unit), the constant output flow of the router;
- is propagation delay;
- N is the number of TCP sessions passing through the router;
- p is the packet drop probability.
- transmission capacity of AQM router: ;
- propagation delay for i-th flow: ;
- starting time for i-th flow (TCP and UDP);
- the number of packets sent by i-th flow (TCP and UDP).
- ;
- ;
- buffer size (measured in packets) ;
- ;
- eight parameter .
6.2. Simulation
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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H | Method 1 | Method 2 |
---|---|---|
0.5 | 0.4975 | 0.4975 |
0.6 | 0.5918 | 0.5918 |
0.7 | 0.7124 | 0.7124 |
0.8 | 0.8098 | 0.8098 |
0.9 | 0.9108 | 0.9108 |
n | Method 1 | Method 2 (ver. 1) | Method 2 (ver. 2) |
---|---|---|---|
0.000992 | 0.000995 | 0.000009 | |
0.003968 | 0.004454 | 0.000010 | |
0.015376 | 0.018848 | 0.000011 | |
0.062462 | 0.076383 | 0.000013 | |
0.262380 | 0.311451 | 0.000014 |
Hurst | Mean | Lost | No. of Dropped Packets | Delay | ||
---|---|---|---|---|---|---|
Queue Length | AQM | Queue | Average | Min–Max | ||
0.5 | 22.93 | 0.49% | 19,261 | 266 | 0.092 | 2.04 · 10–0.18 |
0.6 | 23.05 | 0.49% | 19,270 | 341 | 0.093 | 3.12 · 10–0.19 |
0.7 | 23.55 | 0.54% | 22,950 | 605 | 0.089 | 3.14 · 10–0.18 |
0.8 | 23.31 | 0.57% | 25,943 | 919 | 0.086 | 1.80 · 10–0.20 |
0.9 | 22.56 | 0.65% | 34,040 | 717 | 0.081 | 1.46 · 10–0.18 |
Hurst | Mean | Lost | No. of Dropped Packets | Delay | ||
---|---|---|---|---|---|---|
Queue Length | AQM | Queue | Average | Min–Max | ||
0.5 | 22.30 | 0.49% | 19,391 | 290 | 0.091 | 6.94 · 10–0.18 |
0.6 | 20.99 | 0.50% | 19,306 | 359 | 0.082 | 5.49 · 10–0.18 |
0.7 | 19.92 | 0.54% | 23,275 | 364 | 0.073 | 3.24 · 10–0.18 |
0.8 | 19.17 | 0.58% | 26,873 | 268 | 0.072 | 2.21 · 10– 0.21 |
0.9 | 19.51 | 0.65% | 34,873 | 47 | 0.068 | 2.52 · 10–0.16 |
Hurst | Mean | Lost | No. of Dropped Packets | Delay | ||
---|---|---|---|---|---|---|
Queue Length | AQM | Queue | Average | Min–Max | ||
0.5 | 18.24 | 0.50% | 19,498 | 297 | 0.073 | 7.47 · 10–0.16 |
0.6 | 18.14 | 0.50% | 19,179 | 590 | 0.073 | 9.25 · 10–0.18 |
0.7 | 18.55 | 0.55% | 22,865 | 944 | 0.070 | 1.33 · 10–0.18 |
0.8 | 18.64 | 0.58% | 25,591 | 1409 | 0.068 | 8.04 · 10–0.16 |
0.9 | 19.19 | 0.65% | 33,786 | 1272 | 0.067 | 2.05 · 10–0.18 |
Hurst | Mean | Lost | No. of Dropped Packets | Delay | ||
---|---|---|---|---|---|---|
Queue Length | AQM | Queue | Average | Min–Max | ||
0.5 | 18.37 | 0.50% | 19,290 | 476 | 0.073 | 5.39 · 10–0.17 |
0.6 | 18.06 | 0.49% | 18,790 | 586 | 0.073 | 1.27 · 10–0.18 |
0.7 | 18.45 | 0.55% | 22,964 | 916 | 0.070 | 2.08 · 10–0.18 |
0.8 | 18.09 | 0.58% | 26,124 | 1247 | 0.069 | 1.46 · 10–0.19 |
0.9 | 18.49 | 0.65% | 34,195 | 934 | 0.069 | 6.22 · 10–0.19 |
Hurst | Mean | Lost | No. of Dropped Packets | Delay | ||
---|---|---|---|---|---|---|
Queue Length | AQM | Queue | Average | Min–Max | ||
0.5 | 14.40 | 0.49% | 18942 | 655 | 0.048 | 3.77 · 10–0.12 |
0.6 | 14.45 | 0.49% | 18655 | 705 | 0.048 | 4.38 · 10–0.11 |
0.7 | 14.57 | 0.54% | 22628 | 1051 | 0.047 | 9.87 · 10–0.12 |
0.8 | 14.55 | 0.58% | 25867 | 1147 | 0.044 | 1.33 · 10–0.10 |
0.9 | 14.41 | 0.66% | 34215 | 1027 | 0.044 | 1.62 · 10–0.13 |
Hurst | Mean | Lost | No. of Dropped Packets | Delay | ||
---|---|---|---|---|---|---|
Queue Length | AQM | Queue | Average | Min–Max | ||
0.5 | 10.26 | 0.50% | 19622 | 89 | 0.039 | 1.68 · 10–0.10 |
0.6 | 10.27 | 0.49% | 19417 | 81 | 0.039 | 1.26 · 10–0.10 |
0.7 | 10.24 | 0.54% | 23562 | 52 | 0.038 | 4.94 · 10–0.10 |
0.8 | 10.31 | 0.59% | 27245 | 316 | 0.037 | 1.10 · 10–0.10 |
0.9 | 10.27 | 0.66% | 35145 | 189 | 0.037 | 4.76 · 10–0.10 |
Hurst | Mean | Lost | No. of Dropped Packets | Delay | ||
---|---|---|---|---|---|---|
Queue Length | AQM | Queue | Average | Min–Max | ||
0.5 | 14.48 | 0.50% | 19,163 | 772 | 0.049 | 5.29 · 10–0.13 |
0.6 | 14.52 | 0.50% | 18,935 | 755 | 0.049 | 1.30 · 10–0.11 |
0.7 | 14.59 | 0.54% | 22,590 | 1041 | 0.048 | 4.76 · 10–0.15 |
0.8 | 14.57 | 0.58% | 25,870 | 1196 | 0.045 | 1.11 · 10–0.11 |
0.9 | 14.32 | 0.65% | 34,000 | 867 | 0.043 | 2.51 · 10–0.13 |
Hurst | Mean | Lost | No. of Dropped Packets | Delay | ||
---|---|---|---|---|---|---|
Queue Length | AQM | Queue | Average | Min–Max | ||
0.5 | 10.28 | 0.50% | 19,806 | 62 | 0.040 | 1.19 · 10–0.12 |
0.6 | 10.27 | 0.51% | 20,096 | 55 | 0.039 | 1.97 · 10–0.10 |
0.7 | 10.25 | 0.54% | 23,688 | 70 | 0.038 | 2.23 · 10–0.10 |
0.8 | 10.33 | 0.59% | 27,212 | 355 | 0.037 | 2.04 · 10–0.10 |
0.9 | 10.23 | 0.65% | 35,078 | 125 | 0.037 | 6.33 · 10–0.12 |
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Marek, D.; Szyguła, J.; Domański, A.; Domańska, J.; Filus, K.; Szczygieł, M. Adaptive Hurst-Sensitive Active Queue Management. Entropy 2022, 24, 418. https://doi.org/10.3390/e24030418
Marek D, Szyguła J, Domański A, Domańska J, Filus K, Szczygieł M. Adaptive Hurst-Sensitive Active Queue Management. Entropy. 2022; 24(3):418. https://doi.org/10.3390/e24030418
Chicago/Turabian StyleMarek, Dariusz, Jakub Szyguła, Adam Domański, Joanna Domańska, Katarzyna Filus, and Marta Szczygieł. 2022. "Adaptive Hurst-Sensitive Active Queue Management" Entropy 24, no. 3: 418. https://doi.org/10.3390/e24030418
APA StyleMarek, D., Szyguła, J., Domański, A., Domańska, J., Filus, K., & Szczygieł, M. (2022). Adaptive Hurst-Sensitive Active Queue Management. Entropy, 24(3), 418. https://doi.org/10.3390/e24030418